Automatic process control as a means of controlling the conditions under which a process is carried out is well known. For many years, simple and then steadily more complex closed loop control has been introduced. The control loop uses a formula or model to relate a process output to one or more inputs and, as the output varies, feedback is used to alter the inputs to ensure that the output stays on track.
Certain processes, however, require large numbers of variables having complex relationships therebetween to be incorporated into a model for effective control. In particular some of the variables involved may be variables that are not changeable by a feedback signal, such as the quality of an input product. In such circumstances a fully comprehensive model is difficult to build. Such a fully comprehensive model may be particularly useful in the event that extremely high quality is required in the resulting product. An example is silicon wafer production.
A model is essentially an educated guess as to the relationship between an output and one or more system inputs, The model is required to predict the behavior of the process under different input parameters. Accurate prediction is required if the process is to be controlled to produce desired results. Thus, methods of deriving a model may be referred to as prediction methods.
A particularly useful group of prediction methods comprises what are known as empirical prediction methods. In empirical prediction methods, existing process data, that is to say actually measured inputs and outputs, are utilized to define the model. Different methods use different ways of analyzing the data and incorporating it into a model to arrive at a prediction of an output for any given set of input parameters. The term “data” is used herein to refer inter alia to the quantification of any observable parameter regarding the process.
Applicant's previously filed application no. U.S. Ser. No. 09/689,884 concerns a manufacturing control system, known as a process output empirical modeler (POEM) that uses an empirical prediction method to provide a model as a basis for APC to operate a process, in particular a factory-based production process, The model divides both input and output parameters into discreet sections, builds vectors of all reasonable combinations of the different discrete sections of the input parameters and uses empirical data to associate each of the vectors with a statistical average of actual outputs corresponding to the given vector. The vectors, with their corresponding results are then placed in the form of a lookup table and used in APC as part of a control process that optimizes the inputs that can be varied, in the light of the inputs that cannot be varied, to arrive at a desired result.
Another empirical method that may be used is the method of classification and regression trees, CART. The skilled person will be aware of numerous other methods that makes use of empirical information and to which the present considerations are applicable, such as CHAD and Neural Nets.
A disadvantage of the above system, and indeed of any system requiring statistically significant empirical data, is that it requires relatively large amounts of data before it can begin to run effectively. Furthermore, it is not sufficient to have a large quantity of data. It is additionally necessary to have a good scatter of data across the input space. Certain parts of the input space may be utilized only rarely and it may require a very large number of experiments to effectively fill rarely used parts of the input space. In the case of POEM for example, each input vector should preferably have a statistically significant set of outputs that can be processed to provide a meaningful average output for the given vector.
The data to be relied upon may often be user specific, as different manufactures, even if making the same product, may often insert their own proprietary variations to the process, or may use input materials from different sources, which input materials may behave slightly differently in the process. Manufacturers are not generally willing to provide data sets to their competitors, and system manufacturers generally do not carry out the process and thus do not have their on independent data sets, to sell along with the system.
For all of the above reasons, providers of the system are generally unable to provide meaningful datasets with the systems.
Thus each new purchaser of a system is required to develop his own data set, and until he has done so the system cannot be used effectively. The number of process results required to provide statistically significant coverage of the entire input space is often very high, especially where there are large numbers of parameters involved. Depending on the process, individual experiments may be expensive or time-consuming or both